WASHINGTON, Aug. 14, 2025 — The study of weather patterns, also known as meteorology has witnessed a paradigm shift as traditional methods of forecasting have given way to the study of AI-based multiple weather models.  

Artificial Intelligence is empowering meteorologists to give faster and detailed forecasts with help of robust satellite imagery and shuttling between different weather models to derive the most accurate weather forecasting.

AI models like Google DeepMind’s GraphCast or NVIDIA’s FourCastNet can generate global weather predictions in minutes rather than hours.

FourCastNet adopts a vision transformer model for high-resolution weather forecasts of up to seven days. Designed for GPU scalability, it supports both global and experimental regional forecasting, offering versatility similar to HRRR-Cast, developed by National Oceanic and Atmospheric Administration’s (NOAA) Global Systems Laboratory (GSL).

Weather forecasting is being revolutionized by AI, primarily due to its ability to generate now-cast or near-term forecasts that are localized. This is a major departure from traditional weather forecasting methods, which have struggled to cope up with the dynamic nature of meteorology in the age of climate change and global warming. 

AI based weather forecasting is not just showing accuracy in short-term forecasting but becoming a dependable medium for long-range forecasting. Sifting through decades of historical data to make year-on-year comparison of temperatures, precipitation levels and other weather phenomena could be a cumbersome practice. 

In comparison, AI models function by decoding historical weather data and satellite imagery, enabling high-resolution forecasts without needing as much computational power as traditional physics-based models.

Role of AI in Weather Forecasting You Should Know

Deep Learning Models: US is leading the transformation of weather forecasting by being powered by AI as NOAA’s Global Systems Laboratory (GSL) has prepared a new regional weather forecast system, HRRR-Cast.

This innovative AI-powered system provides highly localised forecasts in the range of one to six hours. It delivers fine-grained, timely predictions essential for localised weather events.

The most emerging trend in AI based weather forecasting are the deep learning models that are trained on radar and satellite data to spot patterns that suggest imminent severe weather, often with higher accuracy than older statistical methods.

Strengthening Weather Stations: Notably, several nations don’t have a well penetrated weather station ecosystem and whatever weather stations have been set up are not suitably equipped for forecasting. This is where AI comes into play with its ability to fill in missing data using satellite readings, climate simulations, and learned patterns, a process especially useful for oceanic and remote areas.

Early Warning System: From Tsunamis to Glacial Lake Outburst Flood (GLOF), nature’s fury knows no bounds and in this sort of a scenario AI based weather models have an edge over traditional alerts.

AI can detect subtle signals in climate and atmospheric data that indicate cyclones, floods, or heatwaves earlier as compared to traditional alerts. An efficient early warning system helps governments and emergency agencies prepare and respond faster, thus reducing the damage in case of a weather calamity. 

Studying Climate Change: Another important application for AI is its role for long-term projections, studying how climate change may see changes in rainfall patterns, intensity of storms or drought frequency.

It can correlate human factors such as haphazard urban growth and land use changes with meteorological data to produce more realistic simulations.

An example of this is GraphCast (Google DeepMind), which has ten-day forecasts more accurate than many leading weather systems and IBM’s GRA, or Global high-resolution model, which is updated every hour. Besides ECMWF + AI hybrid systems blend AI with traditional numerical weather prediction for more robust results.

Can AI Replace Weather Doppler Systems?

The role cut out for AI is to enhance Doppler Radars and not replacing them as most of the nations have spent huge sums over last many decades to set up these radar systems.

Natural disasters such as cloudbursts in many countries have brought to fore the need to enhance the Doppler Radar systems. The use of AI and ML to clean noise, identify patterns, calibrate systems in real time, and optimize computational resources can make Doppler radar outputs more accurate and timely.

As per experts, the way in which independent weather forecasters have earned an edge over many government-run weather stations, similarly AI enhances, rather than replaces, physical forecasting models. 

Traditional radars are missing in geographies that are crucial in terms of the weather patterns they experience.  Such areas are witnessing foray of companies like Climavision, which is deploying proprietary low-level radar systems over there. These systems often incorporate AI for real-time processing and hyper-local forecasting.

There is also a great scope for synergy between private firms and government owned weather stations, an example of which is the private player Tomorrow.io, which is launching radar and microwave-sounding satellites to complement public data and sometimes bridge gaps if public funding or infrastructure is limited.

 

Inputs from Saqib Malik

Editing by David Ryder